Many researchers are interested in applying neural network methods to financial data. In fact, these data are very complex, and classical methods do not always give satisfactory results. They need strong hypotheses which can be false, they have strongly nonlinear structures, and so on, but neural models must also be used cautiously. The black-box aspect can be very dangerous. In this paper, we indicate some of the specifics of financial data in order to prevent some bad uses of neural models.